Field-Level Inference with Microcanonical Langevin Monte Carlo

2023/07/18 by Adrian E. Bayer, Uroš Seljak, Bayer, Adrian E. +3 · 6 citations
Computer Science · Mathematics · Physics and Astronomy · #Computation (stat.CO) #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Markov Chains and Monte Carlo Methods #Methodology (stat.ME) #Scientific Research and Discoveries #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2307.09504

openalex publication_date 2023/07/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Field-level inference provides a means to optimally extract information from upcoming cosmological surveys, but requires efficient sampling of a high-dimensional parameter space. This work applies Microcanonical Langevin Monte Carlo (MCLMC) to sample the initial conditions of the Universe, as well as the cosmological parameters σ₈ and Ω_m, from simulations of cosmic structure. MCLMC is shown to be over an order of magnitude more efficient than traditional Hamiltonian Monte Carlo (HMC) for a ∼ 2.6 × 10⁵ dimensional problem. Moreover, the efficiency of MCLMC compared to HMC greatly increases as the dimensionality increases, suggesting gains of many orders of magnitude for the dimensionalities required by upcoming cosmological surveys.

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